getml.feature_learning.FastProp `dataclass`

FastProp(
    aggregation: Iterable[
        FastPropAggregations
    ] = FASTPROP.default,
    delta_t: float = 0.0,
    loss_function: Optional[
        Union[CrossEntropyLossType, SquareLossType]
    ] = None,
    max_lag: int = 0,
    min_df: int = 30,
    n_most_frequent: int = 0,
    num_features: int = 200,
    num_threads: int = 0,
    sampling_factor: float = 1.0,
    silent: bool = True,
    vocab_size: int = 500,
)

Bases: _FeatureLearner

Generates simple features based on propositionalization.

FastProp generates simple and easily interpretable features for relational data and time series. It is based on a propositionalization approach and has been optimized for speed and memory efficiency. FastProp generates a large number of features and selects the most relevant ones based on the pair-wise correlation with the target(s).

It is recommended to combine FastProp with the Mapping and Seasonal preprocessors, which can drastically improve predictive accuracy.

For more information on the underlying feature learning algorithm, check out the User guide: FastProp.

ATTRIBUTE	DESCRIPTION
`agg_sets`	It is a class variable holding the available aggregation sets for the FastProp feature learner. Value: `FASTPROP`. TYPE: `FastPropAggregationsSets`

PARAMETER	DESCRIPTION
`aggregation`	Mathematical operations used by the automated feature learning algorithm to create new features. Must be an aggregation supported by FastProp feature learner (`FASTPROP_AGGREGATIONS`). TYPE: `Iterable[FastPropAggregations]` DEFAULT: `default`
`delta_t`	Frequency with which lag variables will be explored in a time series setting. When set to 0.0, there will be no lag variables. Please note that you must also pass a value to max_lag. For more information please refer to Data Model Time Series. Range: [0, ∞] TYPE: `float` DEFAULT: `0.0`
`loss_function`	Objective function used by the feature learning algorithm to optimize your features. For regression problems use `SquareLoss` and for classification problems use `CrossEntropyLoss`. TYPE: `Optional[Union[CrossEntropyLossType, SquareLossType]]` DEFAULT: `None`
`max_lag`	Maximum number of steps taken into the past to form lag variables. The step size is determined by delta_t. Please note that you must also pass a value to delta_t. For more information please refer to Time Series. Range: [0, ∞] TYPE: `int` DEFAULT: `0`
`min_df`	Only relevant for columns with role `text`. The minimum number of fields (i.e. rows) in `text` column a given word is required to appear in to be included in the bag of words. Range: [1, ∞] TYPE: `int` DEFAULT: `30`
`num_features`	Number of features generated by the feature learning algorithm. Range: [1, ∞] TYPE: `int` DEFAULT: `200`
`n_most_frequent`	`FastProp` can find the N most frequent categories in a categorical column and derive features from them. The parameter determines how many categories should be used. Range: [0, ∞] TYPE: `int` DEFAULT: `0`
`num_threads`	Number of threads used by the feature learning algorithm. If set to zero or a negative value, the number of threads will be determined automatically by the getML Engine. Range: [0, ∞] TYPE: `int` DEFAULT: `0`
`sampling_factor`	FastProp uses a bootstrapping procedure (sampling with replacement) to train each of the features. The sampling factor is proportional to the share of the samples randomly drawn from the population table every time Multirel generates a new feature. A lower sampling factor (but still greater than 0.0), will lead to less danger of overfitting, less complex statements and faster training. When set to 1.0, roughly 2,000 samples are drawn from the population table. If the population table contains less than 2,000 samples, it will use standard bagging. When set to 0.0, there will be no sampling at all. Range: [0, ∞] TYPE: `float` DEFAULT: `1.0`
`silent`	Controls the logging during training. TYPE: `bool` DEFAULT: `True`
`vocab_size`	Determines the maximum number of words that are extracted in total from `text` columns. This can be interpreted as the maximum size of the bag of words. Range: [0, ∞] TYPE: `int` DEFAULT: `500`

validate

validate(params: Optional[Dict[str, Any]] = None) -> None

Checks both the types and the values of all instance variables and raises an exception if something is off.

PARAMETER	DESCRIPTION
`params`	A dictionary containing the parameters to validate. params can hold the full set or a subset of the parameters explained in `FastProp`. If params is None, the current set of parameters in the instance dictionary will be validated. TYPE: `Optional[Dict[str, Any]]` DEFAULT: `None`

Source code in getml/feature_learning/fastprop.py

def validate(self, params: Optional[Dict[str, Any]] = None) -> None:
    """
    Checks both the types and the values of all instance
    variables and raises an exception if something is off.

    Args:
        params:
            A dictionary containing the parameters to validate.
            params can hold the full set or a subset of the
            parameters explained in
            [`FastProp`][getml.feature_learning.FastProp].
            If params is None, the
            current set of parameters in the
            instance dictionary will be validated.


    """

    if params is None:
        params = self.__dict__
    else:
        params = {**self.__dict__, **params}

    if not isinstance(params, dict):
        raise ValueError("params must be None or a dictionary!")

    for kkey in params:
        if kkey not in type(self)._supported_params:
            raise KeyError(
                f"Instance variable '{kkey}' is not supported in {self.type}."
            )

    _validate_fastprop_parameters(**params)

getml.feature_learning.FastProp dataclass

validate

getml.feature_learning.FastProp `dataclass`